Likelihood Functions

In this lesson, we will have a look at Conditional Probabilities as well as Joint Probability.

In the previous lesson, we implemented an efficient conditioned probability using the Where operator on distributions; that is, we have some “underlying” distribution, and we ask the question “if a particular condition has to be met, what is the derived distribution that meets that condition?” For discrete distributions, we can compute that distribution directly and just return it.


Conditional Probabilities as Likelihood Functions

There is another kind of conditional probability though, which is much more rich, complex and counter-intuitive, and that is exploring the relationship between “what is the probability of XX?" and "what is the probability of YY ...

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